Continuous measure of word learning supports associative model

Abstract

Cross-situational learning, the ability to learn word meanings across multiple scenes consisting of multiple words and referents, is thought to be an important tool for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult to uncover the learning mechanics in part because the standard experimental paradigm, which presents a few words and objects on each of a series of training trials, measures learning only at the end of training after several occurrences of each word-object pair. Thus, the exact learning moment–and its current and historical context– cannot be investigated directly. This paper offers a version of the cross-situational learning task in which a response is made each time a word is heard, as well as in a final test. We compare this to the typical cross-situational learning task, and examine how well the response distributions match two recent computational models of word learning.